GAN 論文大彙總

作者：chen_h
微訊號 & QQ：862251340
微信公眾號：coderpai
我的部落格：請點選這裡

關於生成對抗網路（GAN）的新論文每週都會出現很多，跟蹤發現他們非常難，更不用說去辨別那些研究人員對 GAN 各種奇奇怪怪，令人難以置信的創造性的命名！當然，你可以通過閱讀 OpanAI 的部落格或者 KDNuggets 中的概述性閱讀教程，瞭解更多的有關 GAN 的資訊。

在這裡彙總了一個現在和經常使用的GAN論文，所有文章都連結到了 Arxiv 上面。

• GAN — Generative Adversarial Networks

• 3D-GAN — Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

• AC-GAN — Conditional Image Synthesis With Auxiliary Classifier GANs

• AdaGAN — AdaGAN: Boosting Generative Models

• AffGAN — Amortised MAP Inference for Image Super-resolution

• AL-CGAN — Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts

• ALI — Adversarially Learned Inference

• AMGAN — Generative Adversarial Nets with Labeled Data by Activation Maximization

• AnoGAN — Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

• ArtGAN — ArtGAN: Artwork Synthesis with Conditional Categorial GANs

• b-GAN — b-GAN: Unified Framework of Generative Adversarial Networks

• Bayesian GAN — Deep and Hierarchical Implicit Models

• BEGAN — BEGAN: Boundary Equilibrium Generative Adversarial Networks

• BiGAN — Adversarial Feature Learning

• BS-GAN — Boundary-Seeking Generative Adversarial Networks

• CGAN — Conditional Generative Adversarial Nets

• CCGAN — Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

• CatGAN — Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks

• CoGAN — Coupled Generative Adversarial Networks

• Context-RNN-GAN — Contextual RNN-GANs for Abstract Reasoning Diagram Generation

• C-RNN-GAN — C-RNN-GAN: Continuous recurrent neural networks with adversarial training

• CVAE-GAN — CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training

• CycleGAN — Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

• DTN — Unsupervised Cross-Domain Image Generation

• DCGAN — Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

• DiscoGAN — Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

• DR-GAN — Disentangled Representation Learning GAN for Pose-Invariant Face Recognition

• DualGAN — DualGAN: Unsupervised Dual Learning for Image-to-Image Translation

• EBGAN — Energy-based Generative Adversarial Network

• f-GAN — f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

• GAWWN — Learning What and Where to Draw

• GoGAN — Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking

• GP-GAN — GP-GAN: Towards Realistic High-Resolution Image Blending

• IAN — Neural Photo Editing with Introspective Adversarial Networks

• iGAN — Generative Visual Manipulation on the Natural Image Manifold

• IcGAN — Invertible Conditional GANs for image editing

• ID-CGAN- Image De-raining Using a Conditional Generative Adversarial Network

• Improved GAN — Improved Techniques for Training GANs

• InfoGAN — InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

• LAPGAN — Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

• LR-GAN — LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation

• LSGAN — Least Squares Generative Adversarial Networks

• LS-GAN — Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities

• MGAN — Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

• MAGAN — MAGAN: Margin Adaptation for Generative Adversarial Networks

• MAD-GAN — Multi-Agent Diverse Generative Adversarial Networks

• MalGAN — Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN

• MARTA-GAN — Deep Unsupervised Representation Learning for Remote Sensing Images

• McGAN — McGan: Mean and Covariance Feature Matching GAN

• MedGAN — Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks

• MIX+GAN — Generalization and Equilibrium in Generative Adversarial Nets (GANs)

• MPM-GAN — Message Passing Multi-Agent GANs

• MV-BiGAN — Multi-view Generative Adversarial Networks

• pix2pix — Image-to-Image Translation with Conditional Adversarial Networks

• PPGN — Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space

• PrGAN — 3D Shape Induction from 2D Views of Multiple Objects

• RenderGAN — RenderGAN: Generating Realistic Labeled Data

• RTT-GAN — Recurrent Topic-Transition GAN for Visual Paragraph Generation

• SGAN — Stacked Generative Adversarial Networks

• SGAN — Texture Synthesis with Spatial Generative Adversarial Networks

• SAD-GAN — SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks

• SalGAN — SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

• SEGAN — SEGAN: Speech Enhancement Generative Adversarial Network

• SeGAN — SeGAN: Segmenting and Generating the Invisible

• SeqGAN — SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

• SketchGAN — Adversarial Training For Sketch Retrieval

• SL-GAN — Semi-Latent GAN: Learning to generate and modify facial images from attributes

• Softmax-GAN — Softmax GAN

• SRGAN — Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

• S²GAN — Generative Image Modeling using Style and Structure Adversarial Networks

• SSL-GAN — Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

• StackGAN — StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

• TGAN — Temporal Generative Adversarial Nets

• TAC-GAN — TAC-GAN — Text Conditioned Auxiliary Classifier Generative Adversarial Network

• TP-GAN — Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis

• Triple-GAN — Triple Generative Adversarial Nets

• Unrolled GAN — Unrolled Generative Adversarial Networks

• VGAN — Generating Videos with Scene Dynamics

• VGAN — Generative Adversarial Networks as Variational Training of Energy Based Models

• VAE-GAN — Autoencoding beyond pixels using a learned similarity metric

• VariGAN — Multi-View Image Generation from a Single-View

• ViGAN — Image Generation and Editing with Variational Info Generative AdversarialNetworks

• WGAN — Wasserstein GAN

• WGAN-GP — Improved Training of Wasserstein GANs

• WaterGAN — WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images

如果你對 GAN 感興趣，可以訪問這個專題。歡迎交流。

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